Recommendation systems attempt to provide a user with a list of relevant resources, whether it be music, movies, books, news articles, web pages, text documents, etc. Conventionally, the relevance of a resource is determined based on the user's profile and characteristics of the resource (i.e., in a content-based approach) and/or based on the user's profile and the user's social environment (i.e., in a collaborative filtering approach).
Recommendation systems can be used to excise non-relevant resources from the aforementioned list, and/or to order the list according to a relevancy determined for each resource. The determined relevancy of a resource is sometimes construed as a prediction of a rating which the user would give to the resource.
A user's profile typically includes information collected both explicitly from the user and implicitly (i.e., without any direct action from the user). The profile may provide a representation of the user's interests, beliefs, goals, situation, etc. The profile may evolve based on newly-collected information, such as user purchases and resource ratings provided by the user.
Google provides two examples of recommendation systems which are primarily content-based. Google Reader is a Real Simple Syndication (RSS) reader that allows users to subscribe to RSS feeds and to read articles within the feeds. Google Reader recommends feeds to a user based on the feeds to which the user has already subscribed, the content of articles associated with the subscribed-to feeds, and the articles actually read by the user. Google Ad Sense, on the other hand, suggests advertisements to a user based on the content of a piece of text (i.e., a search query) input by the user.
Amazon.com uses collaborative filtering to recommend items to a user based on items previously purchased by the user. The recommended items include items purchased by other users who also purchased one of the user's previously-purchased items. The suggested items may be filtered further based on ratings thereof provided by the other users.
Conventional user profiles fail to sufficiently capture spontaneous interests or current user actions that do not necessarily correspond to the user's usual focus. Accordingly, recommendations provided by recommendation systems which employ user profiles do not reflect these spontaneous interests or current user actions.
Also, some conventional recommendation systems (e.g., collaborative filtering systems) may base their recommendations on similarity measures which quantify similarities between users or resources. However, these similarity measures do not take into account a context in which resources will be suggested to a user. In other words, the similarity measures reflect an absolute similarity irrespective of the context of the users and/or the context in which the resources will be recommended.
Recommendation systems are desired which may account for a user's situational context and that can be applied to significantly heterogeneous resources. Such a system may introduce a new measure of the interest of a user for a given resource in a given context.